MACHINE LEARNING IN NETWORK SECURITY
USING KNIME ANALYTICS
MuntherAbualkibash
School of Information Security and Applied Computing, College of Technology, Eastern Michigan University, Ypsilanti, MI, USA
School of Information Security and Applied Computing, College of Technology, Eastern Michigan University, Ypsilanti, MI, USA
ABSTRACT
Machine learning has more and more effect on our every day’s life. This field keeps growing and
expanding into new areas. Machine learning is based on the implementation of artificial intelligence that
gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict
values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to
train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we
tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
KEYWORDS
Network Security, KNIME, NSL-KDD, and Machine Learning
ORIGINAL SOURCE URL : http://aircconline.com/ijnsa/V11N5/11519ijnsa01.pdf
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